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A Multi-Objective and Multi-Product Advertising Billboard Location Model with Attraction Factor Mathematical Modeling and Solutions

A Multi-Objective and Multi-Product Advertising Billboard Location Model with Attraction Factor Mathematical Modeling and Solutions

Reza Lotfi, Yahia Zare Mehrjerdi, Nooshin Mardani
Copyright: © 2017 |Volume: 7 |Issue: 1 |Pages: 23
ISSN: 1947-9573|EISSN: 1947-9581|EISBN13: 9781522513612|DOI: 10.4018/IJAL.2017010104
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MLA

Lotfi, Reza, et al. "A Multi-Objective and Multi-Product Advertising Billboard Location Model with Attraction Factor Mathematical Modeling and Solutions." IJAL vol.7, no.1 2017: pp.64-86. http://doi.org/10.4018/IJAL.2017010104

APA

Lotfi, R., Mehrjerdi, Y. Z., & Mardani, N. (2017). A Multi-Objective and Multi-Product Advertising Billboard Location Model with Attraction Factor Mathematical Modeling and Solutions. International Journal of Applied Logistics (IJAL), 7(1), 64-86. http://doi.org/10.4018/IJAL.2017010104

Chicago

Lotfi, Reza, Yahia Zare Mehrjerdi, and Nooshin Mardani. "A Multi-Objective and Multi-Product Advertising Billboard Location Model with Attraction Factor Mathematical Modeling and Solutions," International Journal of Applied Logistics (IJAL) 7, no.1: 64-86. http://doi.org/10.4018/IJAL.2017010104

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Abstract

Location of advertising is one of the most important factors of marketing strategy, as finding the best location to install advertising billboards can have a major impact on profitability of the entire marketing process. This paper provides a billboard location model, which can determine the optimal locations for installing such billboards. The multi-objective and multi-product model developed for this purpose has two objective functions: optimizing the sales profit minus the costs of designing and installing the billboards, and attracting most visitors through maximization of an attraction factor. The designing cost is assumed to be associated with the attraction factor. This model finds the best location of billboards based on constraint such as number of visits and sales volume. Finally, a set of small and large-scale numerical examples are solved by implementing the solution method in GAMS\Cplex solver software. To solve the large-scale variants of the problem, the genetic algorithm.

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